System and method for identifying and processing audio signals

ABSTRACT

A method for phoneme identification. The method includes receiving an audio signal from a speaker, performing initial processing comprising filtering the audio signal to remove audio features, the initial processing resulting in a modified audio signal, transmitting the modified audio signal to a phoneme identification method and a phoneme replacement method to further process the modified audio signal, and transmitting the modified audio signal to a speaker. Also, a system for identifying and processing audio signals. The system includes at least one speaker, at least one microphone, and at least one processor, wherein the processor processes audio signals received using a method for phoneme replacement.

CROSS REFERENCE TO RELATED APPLICATION

The present application is a continuation of U.S. patent applicationSer. No. 16/791,734, filed Feb. 14, 2020, which is a divisional of U.S.patent application Ser. No. 15/810,673, filed Nov. 13, 2017 and entitledSystem and Method for Identifying and Processing Audio Signals, which isnow U.S. Pat. No. 10,566,002 issued Feb. 18, 2020 (Attorney Docket No.W18), which is a divisional of U.S. patent application Ser. No.13/450,739, filed Apr. 19, 2012 and entitled System and Method forIdentifying and Processing Audio Signals, which is now U.S. Pat. No.9,818,416 issued Nov. 14, 2017 (Attorney Docket No. J37), which is aNon-Provisional Application which claims the benefit of U.S. ProvisionalPatent Application Ser. No. 61/477,002, filed Apr. 19, 2011 and entitledSystem and Method for Identifying and Processing Audio Signals (AttorneyDocket No. I77), and U.S. Provisional Patent Application Ser. No.61/479,993, filed Apr. 28, 2011 and entitled System and Method forIdentifying and Processing Audio Signals (Attorney Docket No. I81), eachof which are hereby incorporated herein by reference in theirentireties.

TECHNICAL FIELD

The present disclosure relates to audio signals and more particularly,to systems and methods for identifying and processing audio signals.

BACKGROUND

Hearing loss includes loss of the ability to distinguish between variousphonemes. This includes difficulty with distinguishing consonants, forexample, distinguishing “chicken” from “thicken”. Therefore, a methodfor identifying and distinguishing phonemes is desirable. Also, a methodfor distinguishing various signals, whether audio, mechanical,biological, seismic and/or ultrasound signals is desirable.

SUMMARY

In accordance with one aspect of the present invention, a method forphoneme identification is disclosed. The method includes receiving anaudio signal from a speaker, performing initial processing comprisingfiltering the audio signal to remove audio features, the initialprocessing resulting in a modified audio signal, transmitting themodified audio signal to a phoneme identification method and a phonemereplacement method to further process the modified audio signal, andtransmitting the modified audio signal to a speaker.

Some embodiments of this aspect of the invention may include one or moreof the following. Wherein the phoneme identification method comprisinganalyzing the modified audio signal using a Hilbert-Huang transformmethod. Wherein the phoneme identification method comprising identifyinga time slot occupied by a phoneme and identifying the phoneme in themodified audio signal. Wherein the method further includes transmittingthe time slot and the identified phoneme to the phoneme replacementmethod. Wherein the phoneme replacement method includes determiningwhether the identified phoneme in the audio stream is a replaceablephoneme and, if the identified phoneme in the audio stream is areplaceable phoneme, replacing the identified phoneme in the modifiedaudio signal with a replacement signal. Wherein replacing the identifiedphoneme includes receiving the replacement signal from a table anddetermining a way to smoothly incorporate this sound into the modifiedaudio signal. Wherein replacing the identified phoneme furthercomprising transmitting the modified audio signal to a speaker. Whereinfiltering comprising digitally filtering the extreme values of the audiosignal. Wherein the initial processing includes processing the signaland finding the maxima and minima of the signal, passing the maxima to ahigh-pass filter, filtering the maxima using a high pass filter toproduce a filtered signal, sampling the filtered signal, applying aninterpolation function to the sampled filtered signal to find the valuesbetween the last point and the current point, and determining thedifference between the sampled filtered signal and the signal.

In accordance with one aspect of the present invention, a system forprocessing audio signals is disclosed. The system includes at least onespeaker, at least one microphone, and at least one processor, whereinthe processor processes audio signals received using a method forphoneme replacement.

Some embodiments of this aspect of the invention may include one or moreof the following. Wherein the processor produces an audio stream.Wherein the processor receives an audio signal from the at least onespeaker and performs initial processing. Wherein the processingcomprising filtering to remove noise. Wherein processing includesfiltering to remove audio features. Wherein the audio stream isprocessed by a phoneme identification method and a phoneme replacementmethod. Wherein the phoneme replacement method includes a learningmethod that includes monitoring the audio signal and the backgroundnoise and providing feedback to a broadcast method. Wherein the systemfurther includes a classification method for enhancing the accuracy ofphoneme identification. Wherein the phoneme replacement method includesa learning method including monitoring the audio signal and thebackground noise, and providing feedback to a broadcast method andwherein the broadcast method includes enhancing the audio signal andproviding information to a classification method.

These aspects of the invention are not meant to be exclusive and otherfeatures, aspects, and advantages of the present invention will bereadily apparent to those of ordinary skill in the art when read inconjunction with the appended claims and accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features and advantages of the present invention will bebetter understood by reading the following detailed description, takentogether with the drawings wherein:

FIG. 1 is a top-level breakdown of the replacement phoneme systemaccording to one embodiment;

FIG. 2 is an illustrative chart of the division of the software;

FIG. 3 is an illustrative chart of one embodiment of the broadcastmethod;

FIG. 4 is an illustrative chart of one embodiment of the phoneme method;

FIG. 5 is a graph showing a signal, maxima and minima according to oneembodiment;

FIG. 6 is a graph showing a signal, upper envelope, lower envelope andmean function according to one embodiment;

FIG. 7 is a graph showing an iteration of a signal from h_(i) to h_(i+1)through sifting method according to one embodiment;

FIG. 8 is a graph showing first IMF and residual signals using 64iterations of the sifting method according to one embodiment;

FIG. 9 is a graph showing the results of the sifting method, h_(1k)indicates the first IMF component and the k^(th) iteration through thesifting method;

FIG. 10 is a graph showing the fifth IMF and original signal accordingto one embodiment;

FIG. 11 is a graph showing signal maxima and minima;

FIG. 12 is a graph showing a signal, upper envelope, lower envelope andmean function;

FIG. 13 is a graph showing an iteration of signal from h_(i), to h_(i+1)through sifting method;

FIG. 14 is a graph showing first IMF and residual signals using 64iterations of the sifting method;

FIG. 15 is a block diagram of the filter-based EMD method implemented asa real-time process;

FIG. 16 is a block diagram of the filter-based EMD method using a lowpass filter implemented as a real-time process;

FIG. 17 is a block diagram showing a post-processing high-pass filterEMD;

FIG. 18 is a block diagram showing a post-processing high-pass filterEMD;

FIG. 19 is a Cauchy criterion as a function of iteration number for thethree sinusoid signal IMF₀;

FIG. 20 is a Cauchy criterion as a function of iteration number for thethree sinusoid signal IMF₄;

FIG. 21 is a graph showing the number of extreme values as a function ofiteration number for the three sinusoid signal IMF₀;

FIG. 22 is a Cauchy criterion as a function of iteration number for theacoustic signal IMF₀;

FIG. 23 is a graph showing the number of extreme values as a function ofiteration number for the acoustic IMF₀;

FIG. 24 is a graph showing the IMF₀ of the three sinusoid signal for thefour EMD methods; and

FIG. 25 is a graph showing the IMF₀ of the acoustic signal for the fourEMD methods.

DETAILED DESCRIPTION OF THE EXEMPLARY EMBODIMENTS

The systems and methods include systems and methods for identifyingphonemes and replacing the phoneme with something that may bedistinguished by a user which may include, but is not limited to, one ormore of the following: audio signal, and/or vibratory signal. The systemand methods include transmitting and/or broadcasting the replacementsignal.

In various embodiments, the system and methods may include at least onemicrophone, at least one speaker and at least one processor (See FIG. 1). The processor, in some embodiments, includes instructions for amachine which implements methods/instructions including one or more forprocessing audio signals. In various embodiments, the processorprocesses at least one audio signal and transmits the processed signal.

Referring now to FIG. 2 , in some embodiments, the methods/instructionsmay include at least two components, a learning method and a broadcastmethod. The learning method may monitor the audio signal and thebackground noise. It may provide feedback to the broadcast method toenhance the performance of the traditional filtering in the system. Inaddition, it may provide information which may be used by classificationmethods to enhance the accuracy of phoneme identification. Finally, thelearning method may assist the software which puts together the signalto be played to the user, ensuring it flows smoothly and minimizesdistracting artifacts.

In some embodiments, the processor includes instructions for a machinewhich, when implemented, causes the processor to implement one or moreof the following methods. These may include where the method identifiesphonemes and includes phoneme replacement. Also, these may include wherethe system includes a learning method (s) which may monitor the audiosignals and background noise. In some embodiments, the system mayprovide information which may be used by classification methods toenhance the accuracy of phoneme identification. In some embodiments, thephoneme identification methods may assist the methods/instructions whichprocesses the audio signals and sends the audio signals to the at leastone speaker.

In some embodiments, and referring now to FIG. 3 , the processorreceives an audio signal from a speaker and performs initial processing.The processing may include conventional filtering intended to removenoise and, in some embodiments, remove audio features that may disruptlater steps. The audio stream, in some embodiments, may be fed to both aphoneme identification method and a phoneme replacement method.

In some embodiments, the Hilbert-Huang transform (“HHT”) may be used aspart of the phoneme identification method. The whole sub-functionidentifies the time slot occupied by a phoneme and the particularphoneme being uttered. It passes these pieces of information to thephoneme replacement method.

In some embodiments, the phoneme replacement method determines whetherthe current phoneme in the audio stream needs to be replaced. In someembodiments, the method includes pulling or receiving the replacementsound from a table and determining a way to smoothly incorporate thissound into the audio. The new signal is then passed on to a standardmethod for playing the audio through the attached speaker.

As FIG. 3 shows, in some embodiments, there is two-way communicationbetween the broadcast method's audio pre-processor, phonemeidentification method, and phoneme replacement method on one side andthe learning method on the other. In some embodiments, knowledge of theaudio stream history beyond the scope of the broadcast method may beused by each of these sub-functions. The pre-processor filter settingsmay benefit from in depth characterization of the noise background. Insome embodiments, the phoneme identification method may use an analysisof likely next phonemes to improve selection accuracy. Similarly, insome embodiments, the phoneme replacement method may merge thereplacement sounds more smoothly with knowledge of past sounds andlikely future sounds.

In some embodiments, a phoneme identification method may have threecomponents, see FIG. 4 . In some embodiments, the phoneme classificationmethod decides what phoneme is currently in the audio stream based upona set of characteristics that are currently present in the audio stream.The particular method to determine the phoneme from the classifiers may,in some embodiments, be a clustering method. The final decision ispassed onto the method which merges the replacement sounds into theaudio stream.

In some embodiments, the signal to classifiers method provides the bulkof the classifiers required by the phoneme classification method. Insome embodiments, the instantaneous amplitude, phase, and frequency ofthe oscillatory components of the audio signal may be included in theclassifiers. The HHT may be advantageous for many reasons, including,but not limited to, because of its ability to characterize instantaneousamplitude, frequency, and phase more cleanly than Fourier or waveletmethods. The classification method/process may provide feedback to thesignal-to-classifiers method/process. In some embodiments, theinformation may allow classifiers to be ignored or refined, for exampleto lower the computational load.

In some embodiments, the third component of the phoneme identificationmethod identifies the time slot occupied by a phoneme. In someembodiments, this is passed to the phoneme replacement method, so thatit knows where to insert the replacement sounds. In some embodiments,the information is also passed to the classification method, so that itonly looks at classifiers associated with a single phoneme. This methodmay also help avoid confusion during transitions between phonemes. Invarious embodiments, the signal-to-classifier method and the time-slotmethod exchange information as well. The classifiers may be necessary toidentify the time occupied by a phoneme. At the same time informationabout the timing of the phoneme may be useful for implementing the HHTmethod.

The Hilbert-Huang transform (“HHT”) is disclosed in a 1998 paper, Theempirical mode decomposition and the Hilbert spectrum for nonlinear andnon-stationary time series analysis, by Norden Huang, et. al., which ishereby incorporated herein by reference in its entirety. This paperfurther developed ideas published in a 1996 paper, The mechanism forfrequency downshift in nonlinear wave evolution, also by Huang, et. al.,which is hereby incorporated herein by reference in its entirety.

This HHT has numerous advantages over other methods of signal analysis.It explicitly works with non-linear and non-stationary functions.Fourier methods assume fixed, linear combinations of sinusoids. Thus,the Fourier coefficients exist at all frequencies when describing apulse. In addition, the traditional Fourier transform does not easilylocalize a signal in time. Wavelet analysis offers a method to locallycharacterize the frequency characteristics of a signal, but they use aconstrained time scale and assume linear combinations. The HHT offersmany advantages, including, but not limited to, the ability to identifythe characteristics of the oscillatory components of a signal. Thus, theHHT is suited for identifying the characteristics of an audio signal.Speech is a non-stationary composition of frequencies. HHT mayeffectively characterize consonants, which are more impulsive andnoise-like than vowels.

HHT Method

HHT identifies the highest frequency oscillatory component in a signal.By defining a function, which approximates the local mean of the signal,and subtracting this from the signal, an estimate of the highestfrequency function and the residual may be determined. Once anacceptable estimate of the highest frequency component is determined,that component is assigned as the next intrinsic mode function (“IMF”).The residual is then further analyzed. The HHT method consists of thefollowing steps:

-   -   1) Start with a sampled function, s(k)    -   2) Set h₀(k)=s(k)    -   3) Identify the local maxima and local minima in the current        sampled function h_(i), where i indicates the current iteration        of the sifting method, FIG. 5 .    -   4) Use a (cubic) spline to create an estimate of the upper and        lower envelope of the signal's amplitude, See FIG. 6 . (Note: in        this figure the end points of the signal are considered to be        local maxima and local minima.)    -   5) Take the average of the upper and lower envelope at each        sample point to find a local mean function, m_(i), See FIG. 6 .    -   6) Subtract the mean function, m_(i), from h_(i), to get the        next sampled function h_(i+1), FIG. 7 .    -   7) Evaluate the sifting stopping criteria:        -   a) If they are not met, return to Step 3)        -   b) If the stopping criteria are met, set the j^(th)            intrinsic mode function (IMF) to be equal to h_(i)            (c_(j)=h_(i)), FIG. 8 .    -   8) Evaluate the IMF stopping criteria:        -   a) If they are not met, set h₀=h_(i)−c_(j) and return to            Step 3)        -   b) If they are met, halt.    -   9) Evaluate each IMF using the Hilbert transform to obtain the        instantaneous amplitude, frequency and phase.

The HHT method described above and the papers/books of Norden Huang useone of two criteria for stopping the sifting method (Step 3 through Step7). The first looks at the ratio of change in the IMF since the lastiteration and stops when the difference is less than some thresholdvalue. The second looks at the number of zero crossings compared withthe number of extrema in the IMF. When these differ by at most one forsome number of iterations, the method stops. However, if these stoppingcriteria are ignored, the method does not appear to have a naturalstopping point.

FIG. 9 shows the progression of the IMF estimate for a simple amplitudemodulated signal. Although the desired behavior would be to stop afterthe first iteration, the method seems to transform the IMF into asinusoid of uniform amplitude. The method displayed this behavior forevery signal tried.

Since it takes hundreds or thousands of iterations for the system toprogress to uniform amplitude, while the stopping criteria would besatisfied after tens of iterations; this behavior would appear to havelittle practical significance. However, without a natural stopping pointthat makes physical sense it is difficult to believe that the method'sselection of an IMF is correct or even approximately optimal.

In some embodiments, the HHT may be modified and the modified method maywork essentially as a sifting function in that the method smoothlysettles into a final, fixed IMF. A smoothly decreasing change functionresults. Also, the IMFs have the shape intuitively expected, as shown inFIG. 10 . It may be seen that the IMF mimics the amplitude andunderlying frequency of the signal. Thus, a modified HHT may improve theexisting HHT method and also simplify the HHT method and make it morecomputationally tractable. The traditional EMD method acts as arepetitively applied high pass filter function.

In some embodiments, a modified HHT method may be used in manyapplications in addition to the ones described above. These may include,but are not limited to, biological signals, e.g., ECG, EEG, respiration,myoelectric signals, etc.; mechanical signals, e.g., vibration, impulseresponse, etc; and/or seismic or ultrasound signals.

The traditional EMD method includes the following steps:

-   -   1) Start with a sampled function, s(k).    -   2) Set h₀(k)=s(k).    -   3) Identify the local maxima and local minima in the current        sampled function h_(i), where i indicates the current iteration        of the sifting method, FIG. 11 .    -   4) Use a cubic spline to create an estimate of the upper and        lower envelope of the signal's amplitude, FIG. 12 .    -   5) Take the average of the upper and lower envelope at each        sample point to find a local mean function, m_(i), FIG. 12 .    -   6) Subtract the mean function, m_(i), from h_(i) to get the next        sampled function h_(i+1), FIG. 13 .    -   7) Evaluate the sifting stopping criteria:        -   a) If they are not met, return to Step 3)        -   b) If the stopping criteria are met, set the j^(th) IMF to            be equal to h_(i)(c_(j)=h_(i)), FIG. 14 .    -   8) Evaluate the IMF stopping criteria:        -   a) If they are not met, set h₀=h_(i)−c_(j) and return to            Step 3)        -   b) If they are met, halt.

The traditional EMD method acts largely by high-pass filtering theextreme points of a sampled function. It has a secondary effect ofincreasing the number of extrema in the function. The stoppingconditions for the Sifting Method, Step 3) through Step 7), may vary invarious embodiments. In some embodiments of the traditional EMD methodhalting may occur when the number of extreme points and the number ofzero crossings in the residual function h_(i) are within one of eachother for 3 to 5 iterations. In some embodiments, however, theiterations continue until the change in the residual signal isacceptably small, e.g. the criterion of the equation below:

$\begin{matrix}{\varepsilon > \sqrt{\frac{{var}\left( {h_{i + 1} - h_{i}} \right)}{{var}\left( h_{i} \right)}}} & {{EQN}.1}\end{matrix}$

The EMD method in some embodiments may present challenges for use insome situations because of the need for using the entire signal and thetime it takes to process the data. For example, with respect to signalprocessing, in some embodiments, the method depends upon creating splinefunctions from extreme data from the entire signal. This means that thetraditional EMD method may only be used as a post-processing techniqueonce a signal of interest has been gathered. Also, the method may thenbe too slow for various uses. The traditional method may, in someembodiments, fail to achieve either halting criterion for the first IMFbefore being stopped.

Real-Time EMD Method

Since the traditional EMD method behaves much like a high pass filter onthe extreme values of the signal, in some embodiments, an EMD-likemethod may be used using a digital filter on the extreme values of thefunction. FIG. 15 shows one-embodiments/implementation of this method.

The signal, s, passes into EMD Block 0. In the EMD block, the signal, s,enters a function which finds the maxima and minima of the signal.Whenever a new extreme value is found, it is passed as the next entry toa high-pass filter, H(k). The output of the filter, φ(k), is the nextsampled value of the IMF. An interpolation function is applied to thesamples to find the values between the last point, φ(k−1), and thecurrent point, φ(k). This interpolated function is IMF₀. It issubtracted from the original signal, which has been appropriatelydelayed and the difference is passed to EMD Block 1. EMD Block 1 willperform the same method, in some embodiments, using a differenthigh-pass filter, and pass its difference signal on to the next EMDblock. The EMD blocks, in some embodiments, may be stacked to achievethe desired decomposition of the function. This method may have manyadvantages, including, but not limited to, using a high-pass filter inthe EMD method requires only a single iteration of the sifting method toachieve results analogous to the traditional EMD method.

The traditional method implements its high-pass filter by a low passfilter which is subtracted from the original signal. The resultingdifference signal is then low-pass filtered again. In some embodiments,the filter-based EMD mimics this approach. FIG. 16 shows one embodimentsof this method being implemented as a real-time process. One differencebetween this implementation/method and the traditional EMD method isthat data is processed as it is received and that the Low Pass filtermay be tailored to achieve a variety of effects. This is distinguishedfrom the traditional EMD method where it remains with the low-passfilter imposed by the method. The diagram in FIG. 16 represents one EMDBlock which may be cascaded as shown in FIG. 15 . In FIG. 16 thedifference signal is fed back to the maxima/minima finding method as anew signal. Only after completing a sufficient number of iterative loopsis the difference function considered to be the IMF, while the output ofthe interpolation function is the new signal to feed to the next EMDblock. In various embodiments, this feedback loop gives the Filter EMDthe hidden maxima finding property of the traditional and method.

This same iterative approach of FIG. 16 may be used to implement thetraditional method as real-time processes.

Post-Processing EMD Filter

Both of the methods shown in FIG. 15 and FIG. 16 may be used, in variousembodiments, as post-processing methods. In these embodiments, thecomplete set of maxima and minima are filtered all at once.

In various embodiments, these post-processing methods are substantiallysimilar to the real-time methods, except that the entire data set iscollected before applying the method. In various embodiments, theparticular filter used in any of the described implementations does nothave to be low-pass or high-pass. In some embodiments, this method mayinclude using alternate filters, which may include, but are not limitedto, a band-pass filter, to separate a signal into component waveforms.

There may be various benefits to using this post-processing method.These include, but are not limited to, ability to analyze and evaluatethe filter EMD without concern with edge effects or filter warm up. Inthe following example, the Filter EMD has been applied to both ananalytic signal:

$\begin{matrix}{{s(k)} = {{\sin\left( {2\pi\frac{k}{911}} \right)} + {\cos\left( {2\pi\frac{k}{1001}} \right)} + {\sin\left( {2\pi\frac{t}{1493}} \right)}}} & {{EQN}.2}\end{matrix}$

and to an 18 second segment of recorded speech.

FIG. 19 and FIG. 20 show the measure of change, EQN. 1, for a sample,traditional and low-pass filter EMD methods as a function of the numberof iterations of the Sifting Method performed. The high-pass filter EMDonly used a single iteration. The data is shown for both IMF₀ and IMF₄.The low-pass filter halts before than the traditional method, althoughnot before the sample method, depending upon the value of the Cauchycriterion at which is chosen to stop. By comparing the number of extremato number of zero-crossings, the sample method finishes first, while thelow-pass filter EMD and the traditional method finish within oneiteration of each other, FIG. 21 .

The situation is similar when the methods are applied to the acousticsignal. The high-pass filter EMD stops after a single iteration,followed by the sample method, the low-pass filter method and finallythe traditional method. This is true whether the Cauchy criterion isused, FIG. 22 , or the extrema versus zero-crossing criteria, FIG. 23 .

The actual IMFs returned by the methods differ. FIG. 24 shows IMF₀ ofthe three sinusoid signal for all of the methods, while FIG. 25 showsthe same for the acoustic signal. There is not an established criterionfor judging the correctness of the EMD method. Since the EMD does notuse a set of orthogonal basis functions, there is no guarantee ofuniqueness. This means that the correctness of the decomposition willhave to be judged by each application. There may be many benefits to afilter-based EMD, including but not limited to, the particularcharacteristics pulled out of the function may be tailored by thecharacteristics of the filter used. In addition, the filter may beadapted from iteration to iteration to reinforce elements of interestand suppress items of no value.

The processor may be any processor known in the art. The speaker andmicrophone may be any speaker and microphone known in the art. In someembodiments, one or more hearing aid speakers are used.

While the principles of the invention have been described herein, it isto be understood by those skilled in the art that this description ismade only by way of example and not as a limitation as to the scope ofthe invention. Other embodiments are contemplated within the scope ofthe present invention in addition to the exemplary embodiments shown anddescribed herein. Modifications and substitutions by one of ordinaryskill in the art are considered to be within the scope of the presentinvention.

What is claimed is:
 1. A system for processing audio signals comprising:a hearing aid speaker; a hearing aid microphone; and at least oneprocessor configured to: receive an audio signal from the hearing aidmicrophone; perform initial processing comprising filtering the audiosignal to remove audio features, the initial processing resulting in amodified audio signal; identify a phoneme; replace the phoneme; andtransmit the modified audio signal to the hearing aid speaker.
 2. Thesystem of claim 1, wherein the at least one processor produces an audiostream.
 3. The system of claim 1, wherein the at least one processor isconfigured to filter noise.
 4. The system of claim 1, wherein the atleast one processor is configured to remove the audio features.
 5. Thesystem of claim 1, wherein the at least one processor is configured tomonitor the audio signal and any background noise to provide feedback.6. The system of claim 1, wherein the at least one processor isconfigured to classify the phoneme to enhance accuracy of phonemeidentification.
 7. The system of claim 1, wherein the at least oneprocessor is configured to: monitor the audio signal and any backgroundnoise; and provide feedback; enhance the audio signal; and provideinformation for classification.
 8. The system of claim 1, wherein the atleast one processor is configured for phoneme identification byanalyzing the modified audio signal using a Hilbert-Huang transformmethod.
 9. The system of claim 1, wherein the at least one processor isconfigured to identify a time slot occupied by the phoneme and identifythe phoneme in the modified audio signal.
 10. The system of claim 9,wherein the at least one processor is configured to transmit the timeslot and the identified phoneme for phoneme replacement.
 11. The systemof claim 10, wherein the at least one processor is configured todetermine whether the identified phoneme in an audio stream is areplaceable phoneme and replace the identified phoneme in the modifiedaudio signal with a replacement signal.
 12. The system of claim 11,wherein the at least one processor is configured to receive thereplacement signal from a table and determine a way to smoothlyincorporate the replacement signal into the modified audio signal. 13.The system of claim 12, wherein the at least one processor is configuredto replace the identified phoneme to transmit the modified audio signalto the hearing aid speaker.
 14. The system of claim 1, wherein the atleast one processor is configured to digitally filter extreme values ofthe audio signal.
 15. The system of claim 1, wherein the at least oneprocessor is configured to: process the audio signal and finding amaxima and a minima of the audio signal; pass the maxima to a high-passfilter; filter the maxima using a high pass filter to produce a filteredaudio signal; sample the filtered audio signal; apply an interpolationfunction to the sampled, filtered, audio signal to find values between alast point and a current point; and determine a difference between thesampled, filtered, audio signal and the audio signal.
 16. A method forprocessing an audio signal, the method comprising: receiving the audiosignal from a hearing aid microphone; filtering the audio signal toremove audio features resulting in a modified audio signal; identify aphoneme in the audio signal; replace the identified phoneme in the audiosignal; and transmit the modified audio signal to a hearing aid speaker.17. The method according to claim 16, further comprising: monitoring theaudio signal and any background noise; and providing feedback to abroadcast method; and enhancing the audio signal; and providinginformation to a classification method.
 18. The method of claim 16,further comprising transmitting a time slot and the identified phonemeto for replacement.
 19. The method of claim 16, further comprisingdetermining whether the identified phoneme in the audio signal is areplaceable phoneme and, if the identified phoneme in the audio signalis a replaceable phoneme, replacing the identified phoneme in themodified audio signal with a replacement signal.
 20. The method of claim16, further comprising: processing the audio signal and finding a maximaand a minima of the audio signal; passing the maxima to a high-passfilter; filtering the maxima using a high pass filter to produce afiltered signal; sampling the filtered signal; applying an interpolationfunction to the sampled, filtered, signal to find values between a lastpoint and a current point; and determining the difference between thesampled, filtered, signal and the audio signal.